Interpretable Machine Learning Model for Survival Prediction in Pediatric Adrenocortical Tumors
This interpretable ML model enables individualized survival prediction in pACT using only routine clinical data. It offers a clinically accessible and clinically meaningful complement to existing scoring systems, particularly in patients with ambiguous risk profiles who may benefit from more personalized management.
Authors
- Maximilian Schmutz (ORCID: https://orcid.org/0000-0002-7453-7260)
- Antje Redlich (ORCID: https://orcid.org/0000-0002-1732-1869)
- Eva Jüttner
- Christoph J. Slavetinsky (ORCID: https://orcid.org/0000-0001-5576-5906)
- Michaela Kuhlen (ORCID: https://orcid.org/0000-0003-4577-0503)
- Marina Kunstreich (ORCID: https://orcid.org/0000-0002-2672-4045)
- Paul‐Martin Holterhus (ORCID: https://orcid.org/0000-0002-9308-3129)
- Stefan Alexander Wudy (ORCID: https://orcid.org/0000-0002-7163-5957)
- Christian Vokuhl (ORCID: https://orcid.org/0000-0002-4138-4536)
- Jörg Fuchs (ORCID: https://orcid.org/0000-0001-6145-2391)
- Elisabeth Pfaehler (ORCID: https://orcid.org/0000-0002-7423-0090)
- Gert Warncke
Institutions
- Forschungszentrum Jülich (DE)
- University of Augsburg (DE)
- Justus-Liebig-Universität Gießen (DE)
- University Hospital Bonn (DE)
- University Hospital Augsburg (DE)
- University Hospital Schleswig-Holstein (DE)
- University Children's Hospital Tübingen (DE)
- Bezirkskrankenhaus Augsburg (DE)
- University of Lübeck (DE)
- Otto-von-Guericke-Universität Magdeburg (DE)
Publication Details
- Journal
- Journal of the Endocrine Society
- Published
- 2025-11-15
- DOI
- https://doi.org/10.1210/jendso/bvaf177
- Citations
- 2
- Primary Topic
- Adrenal and Paraganglionic Tumors
- Type
- article
- Field-Weighted Citation Impact
- 4.01
Funders
- Deutsche Kinderkrebsstiftung